Results for “model-adaptation”

Provides an adaptive methodology for adding machine learning models to existing codebases, covering problem framing, data readiness, architectural decoupling, and baseline model integration.

Adapt designs to work across different screen sizes, devices, contexts, or platforms. Implements breakpoints, fluid layouts, and touch targets. Use when the user mentions responsive design, mobile layouts, breakpoints, viewport adaptation, or cross-device compatibility.

Adapt designs to work across different screen sizes, devices, contexts, or platforms. Implements breakpoints, fluid layouts, and touch targets. Use when the user mentions responsive design, mobile layouts, breakpoints, viewport adaptation, or cross-device compatibility.

Use when tuning predator-prey dynamics, regime detection coefficients, or cooldown mechanisms

Fine-tune large language models by training less than 1% of parameters using LoRA, QLoRA, and 25+ adapter methods, enabling efficient adaptation on limited GPU memory.

Visual Prompt Tuning

Adapt designs to work across different screen sizes, devices, contexts, or platforms. Ensures consistent experience across varied environments.

Train or fine-tune sentence-transformers models for retrieval, similarity, clustering, classification, and reranking, with support for bi-encoders, cross-encoders, and sparse encoders.

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

Adapt existing designs to work effectively across different contexts - different screen sizes, devices, platforms, or use cases.

Large Language Model development, training, fine-tuning, and deployment best practices.

TRAK: Attributing Model Behavior at Scale

database-model-standards

Deploy trained machine learning models as production-ready services using REST APIs, containers, serverless functions, and orchestration platforms. Use when the user requests model deployment or provides relevant inputs for this workflow.

Adapts unfamiliar public websites generically, testing search inputs, buttons, openers, and popups before deciding whether a dedicated site profile is needed.

Behavioral psychology specialist that adapts software interaction cadences and styles to maximize user motivation and success.

Chameleon: Mixed-Modal Early-Fusion Foundation Models

Runs directional ablation and refusal-direction analysis for open-weight models the user may modify; use to reduce benign over-refusal or measure refusal/KL trade-offs, not for training.

Mandatory training archive with model gating (APPROVED/REVIEW/DROP). Trigger when: (1) training run completes, (2) need to decide which models to deploy, (3) want historical training reference, (4) need checkpoint recommendations for overfitting.

Diagnose and fix retention problems using the Fogg Behavior Model (B=MAP), covering motivation, ability, prompts, and tiny habits.

Build and sharpen a project's domain model — a CONTEXT.md glossary and ubiquitous language. Use when pinning down terminology, or the agent "uses the wrong words". Repo decision-memory system (INDEX.md, rejected alternatives) → docs-adr.

Provides idiomatic React 18/19 patterns for hooks, server/client components, Suspense, error boundaries, forms, data fetching, state management, and accessibility-first composition.

"Make it interpretable" is four different requests.

Deploy a trained model to serving with versioning, shadow or canary rollout, and a tested rollback path.

Dolphins: Multimodal Language Model for Driving

A model in production is never just weights.

Adapt designs to work across different screen sizes, devices, contexts, or platforms. Ensures consistent experience across varied environments.

Model-trader version compatibility protocol: Embed version metadata in checkpoints, validate at load time. Trigger when: (1) training and live trading versions diverge, (2) models fail to load, (3) action interpretation issues.

Design and implement adaptive filters using DSP System Toolbox System objects. Use when working with adaptive filtering, system identification, noise cancellation, echo cancellation, active noise control (ANC), channel equalization, inverse system identification, or adaptive prediction. Covers dsp.LMSFilter, dsp.RLSFilter, dsp.FilteredXLMSFilter, dsp.FrequencyDomainAdaptiveFilter, dsp.AffineProjectionFilter, dsp.BlockLMSFilter, dsp.AdaptiveLatticeFilter, dsp.FastTransversalFilter, maxstep(), and algorithm selection for adaptive filtering problems. Replaces deprecated adaptfilt.* objects (removed R2020a).

Applied Botany Modeling Skill

Copy-Paste Augmentation for Instance Segmentation

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.

Audits and fixes interfaces to adapt across screen sizes and input modalities using container queries, adaptive navigation, and platform-aware layouts for web, mobile, tablet, and desktop.

Scaling Data-Constrained Language Models